DocumentCode
314406
Title
Selection of convergence coefficient with automata learning rule
Author
Ezzati, N.O. ; Faez, Karim
Author_Institution
Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran
Volume
3
fYear
1997
fDate
9-12 Jun 1997
Firstpage
1978
Abstract
In this paper an approach for selection of convergence coefficient in a backpropagation learning rule is presented. This approach uses a stochastic automata learning rule for selection of the best coefficient in each step of the learning phase. This approach is applied to a nonlinear function approximation problem. Simulation results show that it gives faster convergence than the conventional and adaptive learning rate backpropagation rules
Keywords
backpropagation; convergence; feedforward neural nets; function approximation; multilayer perceptrons; stochastic automata; backpropagation learning rule; convergence coefficient; nonlinear function approximation problem; stochastic automata learning rule; Acceleration; Backpropagation algorithms; Convergence; Function approximation; Learning automata; Network topology; Neural networks; Optimization methods; Paper technology; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks,1997., International Conference on
Conference_Location
Houston, TX
Print_ISBN
0-7803-4122-8
Type
conf
DOI
10.1109/ICNN.1997.614202
Filename
614202
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